{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = pd.read_csv('../homework/iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLength</th>\n",
       "      <th>SepalWidth</th>\n",
       "      <th>PetalLength</th>\n",
       "      <th>PetalWidth</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SepalLength  SepalWidth  PetalLength  PetalWidth         Name\n",
       "0          5.1         3.5          1.4         0.2  Iris-setosa\n",
       "1          4.9         3.0          1.4         0.2  Iris-setosa\n",
       "2          4.7         3.2          1.3         0.2  Iris-setosa\n",
       "3          4.6         3.1          1.5         0.2  Iris-setosa\n",
       "4          5.0         3.6          1.4         0.2  Iris-setosa"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 需求：画一个花瓣（petal）和花萼（sepal)长度的散点图，并且点的颜色要区分鸢尾花的种类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.Name.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_map = dict(zip(iris.Name.unique(), ['blue','green','red']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1140bac88>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6XosK/t4IQtmZjt5+nQSL68IANFqNhA3/fPWwGaHRKo5A00zUQD1cfvWrNaM6/QceuIj/\n83/+iWXLutiy5SWeesrbLG1krWDuuuu3XHrpl2hoaOCzn72ITZs2jJ5buPCNPPvsMxx77PH09LzC\nDTf8mGXLlrNx41Mcd9wJDA3F+Otf/0p7e/u4azZseJpDDumkr287g4O7mTevJd1v7fzCC/VNUJkG\nP01/q9dzGRdDz6dSqAsDYJkWHfP2Z+vAeDeQ4zp0zOkoivvHjyBqoADvec/7ufHGn/DmNx8FwIUX\nXsx3v3sViUSCkZFhLr740gnXHHTQwVx44Xk0NzfT1tbGoYcuHXU9vf/9p/PNb36Niy5ahW3bXHzx\n5zjooMV861tf5/zzz2VkZIRzzjmP1ta9Ru/30Y9+nG9+82s8+OB6RkZGuOyyL4/WGxCEmiGITEMd\nBYvrSA3U4MltT85KFlCpqYydwKIGmovMpTKZ9lxiMSL3r/PSRHMZjpM4+bSyBourQg200jAMg659\nj2DpPssZtodptBpL9uYvCEIVU2Wa/oVQ6o1gFYdlWkTDUXn4C0I1E1TKIV+7ya5PB4txclbZJSzO\nXi7qZgUgCEINEDQ7J7tdk0UkbnvtlnVhbdow5fXVpOlfCGIABEGoGoJm54xr19QEiThWby/mcxpj\nztyps3uqSNO/EOrOBSQIQpWSyc7JSbfOZOeMunPytcMl3N098b6512eTqSBVgw9/EAMgCEK1kJFy\n8CMj5TBZu0QSRoYh4XMu+/o6ov4MQJF1wEuhBjodVc+p+vr97+/mkUcemvFYBKFimCo7Jxz2/rbD\nYf92kTA0NIJfFbsay+4JSv3EAFwXa8NTs7K1u1A10Omoek7V17vf/b6CxiIIFUM+KQfbxo3tIXL/\nfaN/225sD0Z0To7rxiDZ2TmxPmINZvcEpW4MgLnhaSjh1u5iqoG+8MLznHTSqbz++k7uueeu0V3E\nr77ay9q1t9HS0oJlhTnppFMA2LLlJf7hH1by1a9+mX33fQM9Pa9w6KGHcemlX+LGG3/K3nvvzfvf\nv5Jrrvk2zz77F5LJFOeeu4qjjz6W73znSrZvf42dO3fwjnesYNWqCwr+XghCqfDLzhn3sE//bRvR\nOWPH43GwA2QB1SH1YQBsG6vnFew8waNibe0ulhroddddO9pm7ty5XHXV99i1axdXX30VN9/8K5qa\nGrjgglUT+t+69WWuueY6GhoaOfPM97Nz547Rcw8//CADA7u44YZfsHv3bv7932/l4IMP4bDDlvHF\nL/4rIyMjnH76u8UACJVNbnZOOOy9+ef+/VoWRnQOiZNPhfmNJAaTo23qIbsnKPVhAIaHIZmE8EQ1\n0GLqgBdLDdTvnq+8spU3vvGNNDY2YlkmS5cun9B/R8f+NDd789h7731IZAW7Xn55C4cd5l0zb948\nzjvvfGKxPTz77F948snHiUajJBLJgr8HgjArZLJzYrHJNf6TSYjuDUOD/tfXOfVhABobvcCQH0UM\n/hRLDTSbjALo/vsfwJYtLzEyMoxpNvLss39h4cJFOW3zxzIWLVrEH//olWfes2cPV1zxRd7+9mOY\nM2cul132ZV55ZSt33XUnrutWnTaSMBHbsetD8qSOZBtKQX0YAMvC7tgftr4ytQ54EZmpGqgf8+fP\n5yMf+RgXXHAeLS0tjIyMEAqFxhWCmYxjjjmOxx//H84//1xs2+bjHz+PN7xhP1av/hf+8pdNhMNh\n9t//AHbs6KOtbd8ZzVcoP67rsrHv6ZoQPQzEdDT+hQmUTA1UKXU2cHb6YyNwOLCf1npX+vz7gCuA\nFHCT1vqGye5XqBpoyDJwn3yyags8pFIpbr31Fj72sXOxLINPfvJcVq26gMMPf3OZxiNqoLlUwlw2\nbH+K3ljvBNnz9mj7tEqfVsJcAjOFPERVzSUAVaEGqrW+GbgZQCn1Q7yHfObhHwauAd4CxIBHlVJ3\naa1fK9V4qn1rdygUYnh4mHPO+QjhcJglS5bSVUOFKYTCsR2bnj09E1w+xS59WnFU+d92OSl5PQCl\n1FHA1Vrr47OOLQe+rbV+Z/rzNcBjWuvb890nlbLdUGjsh5pIJBgYGCr4DVSYGclkkvnzm4n4baoR\nykIsEeOe5++hKTQxIBpPxXnP4vcQjUjgsw4paz2Ay4HVOcfmAQNZnweBlslu0t8/NO6z49hpN0Qw\nS18JRVSKRSXMJZlM0t8/hGmOFHSfWlqel3sutmMT32OTMOO+5wb7kwyZwcZX7rkUk1qaC8zIBZT3\nXEkNgFJqPqC01n/MObUbyB7VXGDXdO5tmhaOM0IiMYwZaFlb/odm8SjvXBzHxnGcgN93YbawTIuO\nOR2+MYB8pU8DZwvZdmHulUKuL7RvIS+lXgGsANb7HH8WWKyU2gvYk2539XRv3tjYPPowmoqWlmZ2\n7twz3S4qknLPJRQKy8O/Qlne5u1o9csCyiZwtlBQ/f18FHJ9oX0LU1JqA6CAF0c/KPVhYI7W+nql\n1GeBdXiCdDdprXtm0oFpWoEeRpFIpGbiBbU0F6G4BC19urHvaXpjvVimhWV6MYPemCeNkp0tFFR/\nPx+FXF9o38LUlNQAaK2/k/P5V1lf3w3cXcr+BaFesUyLqOkf8A2SLeQ1TOvq57pdgkqoFHJ9oX0L\ngag/OWhBqHOG7WGSjr+uftJJMGxPoasPwfTzC7m+0L6FQIgBEIQ6wXZsYskYYSNM2PRP3w2bERqt\ntHxCoTILQfX7/WpzzKTvItf6qAfqQwpCEOoYv4BvLLmHaHjOODfQhGyhQmUWpqHfPyG4O52+JVg8\nY8QACEKN4xfwjYbnjBqBybKF/PT3p6OfH1S/3y+4G7RvCRbPHDEAglDD5Av4WqZFNDyHkw88laSb\nzL8PoFCZhaD6/X7B3SB9BwkWC3mRGIAg1DBTBXyTbpJoODq1RlBGP3+mmTeZ65PJ6Qd3J+tbgsUF\nIQZAEGqYRqsxWMB31gZUZP1+qQdQEGIABKFCyWTt2M70s1oy1wJ0zOnAccfvlp9KHiKWCNjvdDNv\n0sFdcnfvZ4K7MPF+iQT09Xn/5/YLk99P9gpMisQABKHCKKSoi9+17dEOFjQvoDfWG1geommHRXyP\nnb/fAjJvfIO77e3gQmTdvWPHFrRjvqAJd2sYGYaGRpKdnTgHL8batm3ctfaCdqxeKfQ+XcQACEKF\nEVSmIei124Z6aY+2c9qidwWWh2gKNZEw43n7LSjzxie4a23eOOF+DWv/HWNgALd9fwjNASDy8IO4\nG54idezxY/1u24bd3k7itHeJaNw0EReQIFQQmaydbDVPGJNpmMwtM9W1QN6A77T6zWTe5NTAzmTe\nTMcdlCnMPuF+qSRmTw/mUBwy7ivXxhwawuzphexSqJl+obBAdR0iBkAQKojAMg3lvLbYmTd+94sN\nYaSS4KTGHvYpGxwbI5UY9f8X1K8gLiBBqCSys3Yc1yFhJ4hYEUzDzJu1k9H0DyzxMEW/U147G5k8\n0WbcUBjDtCCUfkyFLDAt3JA5unIoqF9BDIAgVBKWadEebeexbY/SP9yP7SSxzDCtja0cveAd49w3\nM5Z4yNNv4GIyhUpETOjc536hME5HB8bAAGTGY1g4zc24LS1jRqGQfgUxAIJQeRjgukCmXreb/jw+\nu6YQiQc/sovJxFNxbMfOe22hEhFB7jey8h8nZAElVhw/MQtIMn5mTGADoJSKAnuR9VuotX65FIMS\nhHrFdmx6Yz0c1LoYx7FJOEkipleBrTfWwzJnOZZpFS7x4EN2MZm5rWEG+5P5ry1UIiLg/ewj3kwy\nkYCBAWhpgYjnKrKXS5nIYhDIACilvgJ8HujLOuwCbyrFoAShXskEYy2zCdO0aMx6AGeCsVEzOq5d\nLtkSDzPBMi2ikWiwAvJZmTxFwe9+kQi0tZW23zol6ArgbGCh1npnCcciCHVP0GDsuGBxzkqhJBIP\n+Qqzl6vYuxSKLwpBDUAvMFDKgQiCEDwY6wWLO3is90/0j/RjOyksM0RrQytHtx8b2O0zJfl2/C7r\nwtq0YfaLvYv2f1GZ1AAopa5If7kL+LNS6l5gdAeG1vprU1z/JeDvgQjwI631jVnnPgN8gjG30ie1\n1nraMxCEGiM7GDt5INdNP/QyDz4j/dmlWOTb8Ws+pzHmzJ31Yu+i/V9cploBZH6z/sfn2KS/ZUqp\n44GjgXcAzcClOU2OBD6qtX4i0EgFoU7IDsbmk27wgsW9HDT/4An7BXpjvSxzugpfBeTT2gfC3d2k\njjpq/MFSF3uXQvFFZ1IDoLVeDaCU+pjW+pbsc0qpC6e492nAJuBOYB5eEDmbI4EvKaX2A+7RWn9z\nspu1tjYTChX2w21rm1vQ9ZWEzKUyma25xBIxmnZ4mj0eYwHReCrO3NYw0UhhQdK2uWFosqApJ9A8\nPAymDU2hiZuv4nGYG84foI3F/O9Z4mtr6XcMijefqVxAl+A9vD+llFqYc91HgB9Ocvk+wELgvcAb\ngbuUUp1a68zK4Tfp63cDdyql3qu1/l2+m/X3D001l0lpa5tLX1+ArIYqQOZSmczmXGzHJr7HJmHG\nfc8N9ieDZfHkoa1tLn2DSSJxGxI5fTgOIcciFU/BSM452yYxmIShPH3btv89S3htLf2OwfTnM5mx\nmEoL6AU8l0/uvxG8zKDJ2Ams01on0r79YaANQCllANdqrXdorRPAPYA48IS6Jaj2fyE6/9Mmn3Y/\nkOzsJHdj2uiOXNueqN/vd0/H8VYT6a+nda1fv+L+mTZTuYB+B/xOKfXvWuvuad77EeBipdT3gAV4\na9RMGuk8YLNSagkQA04Ebprm/QWh6gmq/e+v89/OguZ2emNTBYtnTt4dv35ZQAvaMXU3DXf95zj9\n/uTKM8dJRtjLujCf04S7u8faqUMw9e6pry3yDuR6x3DdqTMGlFLPA9nm1QXiwLPApVrrLXmu+zZw\nAt5K43Jgb2CO1vp6pdRZwKfxVhPrtdZfmWwMfX2DBaU21NIyUOZSmcxkLhu2P+Wb8tkebR+nwT9Z\nu8mCxTNlwlwC7AMI33E7Yd0NVtZ7pZ0iqTpJnvGh0UPWhqfSmTsuJJIQCWM9+ies3QM4C/af9Nop\nxxNkLlXODFxAefNjg+4DuBd4kbG39I8AbwHuBm4ETva7SGt9Wb4baq3XAGsC9i8INUc+OYeMBv/S\nfSaXfchuN9Ndv4HJt/M2czyR8N7oQzmPFCtEuLvbk3OIRCZm8jRakEoS6u3FNQzPvZN548+9Nsh4\nhGkRtB7AMVrra7XWu9P/fgws11rfiacPJAjCNAmqwV+Izv+sMTDguW78GBn2zkNe7X+SSQzbBjuV\n/1qh6AQ1ALZS6rTMh/TXCaXUG4BwSUYmCDXOVLIPYSNMLBmbkc5/4KByMsHQQB929kPZr9D7VMXf\nW1qgIY/8REOjdx7yav8TDuNaFuDCUAzs5MRrZ8J0i9bXGUFdQB8HblZK3YoX+n8eLwtoFXB1aYYm\nCLVNPtkH7+G9h/tfvm/aOv+Bg8qOw98euo3YS904iWHMSCPRhYrFrQprWy80WUTiNnZ7B+B6PvvJ\npBciEZKdnf4xgM7OMRdOHu3/1IIFWBueJrStF8O2cS0Lp7WVkfefPtH9E4RsyYjMXEQyYgKBDIDW\nejNwlFKqFbC11rvTp/6tZCMThDrAT/Yh+2E/XZ3/oAXl//bQbcS2dGOEQljpguvGYw/RF3ma/bqO\n9zZbJeKEH/sTYOAcdPCU0gvJlWfC2tvGZ/dkMnmy8MvkcVpbCbW0QP8uvBwTI22wZpb7MU4yIj0X\nkYyYSFA56CPwsnj2AgylFABa6xNLNzRBqH1yZR/CRpj7X75vRjr/gYPKyQSxl7yH/yiOQ9PuIYbd\nGE4qCTSBY2P29wMGTnZwNp/0gmmSPONDvvr9OZMer/1vmjQ/8Tj20i5wsrJ7TIuwfs4/CDwZIhkR\nmKAuoF8APwU2U0ylKUEQgPQD3owSS8ZmrPM/VY2ATC2BkaEBnMTw6Js/gJlMea4XxyY5MgTM81I0\nM0HZRGK87EOmCLtfJo6ffr/vpNOZPH193oohNAdMC5qz7pkJAge5X4ZMoNnykYyYbNx1SFADMKS1\nvq6kIxEEYXrF2Wd4bUNzC2Zk/H2ccAjXsjBMk3BDs3cwEk77842Jb+DFLMIeNIAclGIXra9hghqA\ndUqpfwacY4wSAAAgAElEQVTW4Uk6AFISUhCKhe3Yo5u5JqsHABBLxnxdQFPWEnCBWAyrsZHook4v\nBpAJ2Jom8XnNtERaMEPpxD7TC8SCUXjx98k2bgUNIAel2EXra5igBuCs9P+fzTomJSEFoUD8JR46\nWNC8gN5Y77hjruuy7qV7J83u8a0lEG3nzb1gPX7vaNB18YJDeN51iW3Ro1lA7tHH0daqYFuvp65p\n2ySPPpa8WUDBJhiogEvQAHJQxgWa03MRyYiJBJKCqARECmIMmUtlUmwpiGyJh807NgaSjMiQvaKI\nbNro/zbc3k7i0MMYGRqgobkFK+M2sW3a5obpG0yOvS3PsATjqOyDT9++2ThTBZCni99cqpxiSkEE\n2gimlGpVSt2glHpAKbW3UuompdT8wCMQBGECmayd7Ic6jGXtAKMB38na+W328rKGolhu+i3YzPlT\nT2fEWKZFc0vb2MMfxoKz2Q9Mv2NTTtCetG/fzVmZAHIxHv4ws3HXEUF3At8A/C+emNsgsA24tVSD\nEoR6YFakIPykF0YvTmfElIpy9i0EIqgBeKPW+nrASev7fxnYf6qLBKFesB2bWGJq6YVsgmbtBJWM\nyO47kUrQN9RHImROnhETDpdOKmEm2Tgi3TCrBA0Cp5RSLaT3ACilFgMTq0QIQp2RHcRt2mER32P7\nBmf9mDJrJ53lMx3JiAXN7Ty/S6P7NSP2MA1WI8ekmjiFQzHHZdjYuLE9RO6/b3KJh0KYTjZOwGCx\nUFyCGoArgAeBA5VSvwXeDpxbqkEJQrWQLb3QFGoiYcZ9pRfy4Zu14yPxEFQyYu3z/87ukQEWzN2f\nkOlt9Hp0nyHY8Qyncdjow9WN7cGIzvEewlNIPBRC0AIu46QbSjgeYTxBtYDWKaWeAN6KVxjmk1rr\n10o6MkGocIJKL0xGrhREvqIuQSQjUnaS3lgvBgaO64yuFiwrzCNtcd7x5pOIpBwIh703/8mkEopF\nruyDXxaRSDeUjaArALTWO/Bq9wKglNqktV5WklEJQhUQVHohCBkpiKDt/CQjYqkhkk4Sy7CwnRSm\nNeZ/H7GHGbD30BZt83zsU0klFJvJCriIdEPZCBoE9mNRsQYhCNVIdnDWcWyGU8M46UDsVLINMyUT\n3DVdc0JgOBpqJmyGsQwLyxz/btdgNdJizfEe/uHwtAPDQesLzAiRbigbgVcAPlTHDjJBKBGWadEe\n7eCx3j/RP9JPw26LkWGb1oZWjm4/tmj1eQEcx2Ht87fR/Xr3aHC32Wpiyd6HYqWDuyErTHu0nd0j\nA+ODxXaSY3Y0M2f9ev8YwFjDscBwVj2AJxe49GTtSg4a5A6MSDeUjUIMwJQopb4E/D0QAX6ktb4x\n69z78ILLKeAmrfUNpRyLIJQGN52lYqRfiYz05+K+H619/jb0692EzNBocHcoNcSzO59hyT6HjT6c\nVy7+x4lZQDuaOSV06LgAqxGdM2YE/IxCWkP/5e4/Ee81sA49eNL6AoUSNFgsFJdJDYBSyiFTnWGa\nKKWOB44G3gE0A5dmnQsD1+AVlo8Bjyql7pLAslBN2I5Nb6yXg+YfjOM6NEUt4jEb0zDpjfWyzOkq\nyiogkUrQnX74ZxOywgzZcU7Y/yQcwxkNIB+x35tJpBIMJAZoseZ4b/65b9GWhRGdQ+LkUyGZ9A0M\nO67NzpF+5m432K0cXMt7O59OkDswQYLFQtGZ1ABorQuJEZwGbALuBOYBn886twR4QWvdD6CUegRY\nAdye72atrc2EQoX9QrS1zS3o+kpC5lJ+YokYTTu89M8MjWnl4ngqztzWMNFI4cHL7Xu2Y0ZsopHm\nCecGEyM0tsC+c/abcK6DvT1fflP6jT6XeBzmN0LUv11TNESkyaIxCa0NFnbzmC++mPObDar1dywf\nxZrPVCuAKyY7r7X+2iSn9wEWAu8F3gjcpZTq1Fq7eAZhIKvtIDCp6Hd//9Bkp6ek3kXHKpVqnovt\n2MT32CTMOAAt85oY2B0fPTfYn2TILHxuyZSFk7CIpSbKKjiORXLQoi+epx/bJhK3IRH3PZcYTMLQ\n4IR2LS1NxGMpEnEb2zXoH7FxU2P3KOb8Sk01/475MQMxuLznpnrDN6b4Nxk7gXVp6QiNV0cgU9Zn\nN5A9qrnArinuJwgVRWaHruOO3xTvV6w9w0yyaSKhCJ17dWI7qZx7pejcq5NIaBLhtHSAFcfBcdOZ\nSq49McCa1S6DaVjs3dDK4L6tXpP4MIbtTDo/obqYygW02u+4UsrAe6ufjEeAi5VS3wMWAFE8owDw\nLLBYKbUXsAfP/XP1NMYtCBVB9g7deCqO7di+O3n9dP+nk02zcvGZE7KAOvfqZOXiqfXyU8u6eP51\nTeyl7lHt/+iiTt64rGvcW5yfhv6B6hhG+jXRh/93/LXHdQX/JgkVS6B6AEqpi4Ar8R7iGf6mtT54\niuu+DZyAt9K4HE9NdI7W+vqsLCATLwvoh5PdS+oBjCFzqTxsx2Zua5jB/qTvm/Fkuv/TyaYZDe5G\nWiZ/8/fp23LATCRwIhFsk/x9Z2noW5u9WgKO4ZKwk0SsMKZr5Nfzr0Bq5XcsQzHrAQRNA/0c0AV8\nA+9BfjxwylQXaa0vm+Tc3cDdAfsXhIrGMi2ikaivT7wYkhEZIqEIbaHgBdKz+3YtsJu8QK4J+fvO\n7Nod3DUq0WACjZkkDAORaKgRgmb5bNda/w3YCCzTWt8MqJKNShBqiIL0/MvZt+j51zxBDUBMKXUC\nngF4n1JqP6C1dMMqDyJFLgTBL5DrVw8g0y5shAPp/peCoDUH/C/OkmhIjMD27d7/IBINNUJQF9A/\nA5/AcwWdC2jgK6Ua1GzjurBxo0lPj5HZE0NHh8vy5Y5IkQuj+BdwbwcMemNj9QDaox2AO66oe7Z0\nc4bZyKYJWnPA/2ILe8ECGn/6Q6wtWzCSSdxwGHvhQoY/eaG4f2qAQCsArfVf8DZyHQ6sBlq11teW\ncmCzycaNJr29BpY1tgGxt9dg48ZC9sEJtUa29n9jqAnLtHhs26M81vun0XoAlmnxWO+feGzbo+Pa\nRcNziCX3eMXa09lC7dH2CdlCpWB52+G0R9tn1Hdo/R8wt78GRrqymGFibn+N0Po/lHzcQukJtAJQ\nSp0C3AL04tUDmK+UOlNr/b+lHNxsYNvQ02P4SZHT02OwdKm86Aj+gVzHdegf7gfcURVQx7HpH+mH\nXE3+tBE4+cBTSbrJvLr/pSBozYEJxOOEN2+CvdpwXRfXtr0/BsMgvHkTI/G4/w5joWoI+op7DfAu\nrfVRWusjgDOAH5duWLPH8LAnheJHMilxLsHDL5iasBPYThLbSZFwvF+iRPqz7SRJ2OPbJ50ESTdJ\nNBwtyyYqzwhNo+/t2zFG0j5/w4BQaLQ8ozGSjgkIVU1QAzCitd6Q+aC1fpwZCMRVIo2Nns/fj3B4\nenGuoEFkCTbPLsXQsvcLpkasCJYZxjJDREzvlyiS/myZYUJmKL3z1ttdGzYjmK7pFWv3kXWYrblM\n3Un6F3TvvXEbGnybuA0NsO++pRuDMCsEDQL/t1LqZ8ANePLNHwJeUkqtANBaP1yi8ZUcy/ICvr29\nRq4UOR0dbiD3T9AgsgSbZ5dCd99m4xdMNQ2T1sZWcF3M9Fu1aVrMj8xnW6yXp7c/he0kscww8yPz\niSeHeOK1xyfs5DXNqd/DijmXSToZK8yeqQfQtg+hHTvAynpLslMkly0X908NENQALEn/f1XO8dV4\nctEnFm1EZWD5cgfwfzAHITuInDEYvb0GYNLV5Uy7nVAcsoO2xdCy9yvMfvSCd5DJAspIQezb/AbA\nYFeiP32ly6a+pwlbEdqzirXr17tZ+/xtnKE+NOtz8WNcYfZ0PQD7uJPgofVYfTswRkZwGxpILlvO\nyCWXTn1DoeIJWhT+hFIPpJwYBnR1OSxdOn0p8qBBZAk2zy7F3H2bYbJg6jJnOXNbw+zaOcz9L9/H\nwY0tOI5NwkliugZ/3fUCyVQqJzAcovv1bhKpxKSyDqWYy8RO8hRmD4exTziF+IrjYedOz+0jb/41\nQ9AsoIXAz/DqAB8L/Ao4R2v9UslGVgYmq1udj0wQ2e/hnQkiR6PB2wnFoZgF23PxK+CekYLocwdH\n+zVNi0bTYmBkYPJi7YmBSeUdSjmXsU6mKMxuGLBwYWF9CBVH0CDwT4Hv4Cl3vgb8GvhFqQZVTQQN\nIhcz2CxMTUE7YIvc75TF2iOTlsKYnblIYfa6JKgB2EdrfR+A1tpN1++dV7phVQ+ZILKT48LPDSIH\nbScUh6m0+oEZZ9NMlomT3a/jePr7pmHSHm0nGmrGwCVpJ3BdO5ief+49XWc0s6goO4kzGT8woR4A\nIIXZa5ygQeC4Ump/0pWulVLHACMlG1WVETSIXGiwWZgefkHb9mgHruuy7qV7p51NEzQTZ9k+XTzX\nr8dp9y/bezlbB7eyeecmRpwRGswGlu2znNMPPiPQXPzu2blXJ8sWzlCXPzvjJ1OEvb0De8ECLxCc\nrgcghdlrm6D1AI7CiwEcBPwV2As4Q2v936Ud3hjVUA/AtoMFkYO2y0ct6ZvPys/FsUeDtpt3bJyx\nLv9Umv6ZuWTagbdZLGJF+NvAi+C6LGx5I7FEjGgkOroyCJLFM3pP1yXhJL19B4Yx7XoCGawNT3kP\n+pzcZ7u9HXvp8tF6ALXw5l9Lfy9Q3HoAQV1AJnAr8DbgdWAOsH/gEdQJmSDyVH8zQdsJxSGzAxa8\n1UD2AxzGsmkmcwdlMnGmuja7nWmYNIY833n/cD/9I/2YGLQ0thAyQ4H6nXBP06Ix1IhpWoGvn3jD\ndMZP7v4D0/SOg/yC1glBDcD3gf/GKwqzO/3/F0s1KEEoBYVo4we9NqhkRNB+Cx23/w1F51/wCLwC\nSO/2fQ+wVmu9leDxg6rGT7ZBpByqk6myacJG2F/nP4Cmv+mabN+zHdM1A0lGZF+bL4unZPUEJONH\nSBP0IT6klPoc3o7fi5RSFwO141TzwU+2ob3dC0P09oqUQzWSTxvfe9Du4f6X7xsXLA6i6Z+yU3Tv\nfIYnXnscM2LjJCyarSY69z6UkOX9eflJRkB+TX6/YHNR6wlYFnZHh38MQDJ+6oqgBuAjeIVgVmqt\n+5VS7cCHp7pIKfUknssIvCLyH8869xm8IjN96UOf1FrrwCMvMX6yDY89ZgEuBx3kipRDleKXGZT9\ncM1stnqs909gGBw0/+DRYxlN/2h4zui13TufYcgeImSGiUaaiaUSDNlDdO98hiX7HOYrGZGbQZSL\nn+yDX9/5rg9CJrNnXBaQZPzUHUGlIHqAr2V9/sJU1yilGgFDa318niZHAh/VWj8RZAyziZ9sg+NA\nf7+R/todfXESKYfqIlfOIWyEuf/l+8a/WQfU9Dddkydee5xQjlsnZIYZsuOcsP9JOIYzQTJiMk3+\nfLIPRa8nYBjYXUd4hd0LSUkTqppS+vG7gGal1H3pfi7XWv9X1vkjgS+l6wvfo7X+5mQ3a21tJhQq\n7Be0rW1uoHaxmCd3ki15MjwMGWXcpqbxbtJ4HObOnV0ph6BzqQbKOZdYIkbT6141rwzDqWEadlvg\nQlPUGs3kAYin4szfu5FoZG/P5x+xiUaaR89Hmz3f+mBihMYW2HfOftMfz47x4/HrezaQ37HKpVjz\nKaUBGAKuxts/sBi4VymltNap9PnfAD/EcxHdqZR6r9b6d/lu1t8/VNBgppM7a9sQj1skshIlHAdG\nRjwXUDzuMDIyvv3goM1QYUMMTC3lNZd7LrZjE99jkzDjo8ccx2Zk2AYM4jGbESM+rv1gf5Ihc5Bk\nysJJWMTS2v7R5gixoUT6HhbJQYu++PTm5jcev75LTbl/LsWkluYCM9oHkPdcKYvePgf8Mi0d8Ryw\nE1gAoJQygGu11ju01gngHqA4mrZFIFu2wXHGsuJaW11aW928dQMSCejrY5zh8CNoO6H0+ElGmKZF\na0MrrY2tkxZSj4QidO7Vie2kxt0zqMRD0PFk9w0zl7AQhFxKuQI4B1gGXJAOGs8DtqXPzQM2K6WW\nADG87KKbSjiWabNsmcNzz4Xo7jYYGfHcP52dLgcf7LBt2/gsoKVLHW6/fWLblStTE4zF2rVTtxNm\nF1+d//Zjyc0C8gu6rlx8Jmufv43u17sZTIzgONZooZdijqcQCQtByEcgKYiZoJSKADcDB+JpCH0B\nT056jtb6eqXUWcCn8TSF1mutvzLZ/WZbCmLDBjOd4eO9qUfSL3Pt7d4DPztudvvtIbQ2x8XQbBuU\ncjjjjLG3w6Dtij2XSqaS5pItGZF5y/c75kcilSA81yY5aM3ozX+q8RQiYTETKunnUii1NBcorhRE\nyVYAaddObqroY1nn1wBrStV/IeRmAWUHfDMZP5mAbyIB3d0GoZzvpGV5xzPGI2g7oXzk1fkPoLUf\nCUVomzN32j7/IOOZlYIwQl0ijgcfMsVb/MgUb8kwMMC4gHA2IyPe+em0E4Rcii4FIQhpxAD4MJ3i\nLS0tY+mhjuMZiIykekODdz63XS7Z7QQhl3IVtxFqHzEAPkyneEskAkq5bNsGL75o8OKLJi++aLBt\nm3c849aJRLyAb65+kG17x8X9I+Rjqswgcf8IM0UMQB6WL3dob3dHtftt2wsA+xVvWbzYYd48Tz/I\ntl1cF+bN845ns3JlCqUcUimXWMwllXJRymHlyuABYKE+Wd52OO3Rdi8wnIpjOzbt0fYZS0EIAtSJ\noudMMAzo6nJYunTynfK2Ddu2GRx7rE0q5e0ijkYhFPKOL18+dp1pwhlnpEgkPJ9/S4sEfoVg5EpY\nFCwFIQiIAZiSTPGWfGQCxpblPfSzffmZgHHu9ZEItLWVZrxCbRM0K0kQgiAuoBmQXQ8gO2CcSnlv\n9qm0Ryc3YCzUBpMVhReEakJWANPAr0ZAR4fLggUua9eGxtUJaG/3dviKwGLtELQovCBUC7ICmAbZ\nNQIyMYHeXoP160127/biBpZlYBiwezc8/7x8e2uJbJ3+xlATlmnRG+tlY9/T5R6aIMwIWQEExK9G\nAHipoZs3mxxyiPeGaNtemqhpgtayw7dWCLIbVxCqDXlFDUi+3cGxGIyMGKRS3kM/HB6rsic7fGsH\n2Y0r1CJiAAKSb3dwNAoNDe4EjR+QHb61hOzGFWoRMQAByd4dnJ3tY5qedHTu8cwOX8sayxjKkJ1F\nNBlB21UrtmMTS1RHNo3sxhVqEYkBTIOlSx3uvz/Epk0mIyMGDQ0uy5Y5XHhhivPPD/HMMyGSSYNw\n2GXJkhTvepfDunXWuMwgYFy2UEeHt7s4O4kkX7ZRbrtqJTubpmmHRXyPXRXZNH46/YUUZheEciMG\nYBrccUeIeNxk8WJGg73xuMn554eAEIcdBsmkSzgMu3aFuOEGhw98wB4NHD/2mFdS8qCDxvSEvJoD\nJl1dY2+W2dlGk7WrVrKzaZpCTSTMOL2xXoCSaNsXC9mNK9Qa4gIKSEbPP5Phkwn22jY880wI0/Q+\nNzR46aDDwwZbtpijZR8dB/r7Dfr7jXEic6bpZRdl3DyZbKPcCmG57aqVTDZNdmETGMumqRZ3UDQc\nlYe/UPWIAQhIPj3/eBySSWNcfV/b9tw4ySTs2uUdSyS847Y9sRZwdo2B6dQiqEYkm0YQKgcxAFn4\nBV0zx+bMGdPzt20YGvL+b2qCcHhMztm2PbeNYXirhKYm2L7dO5dx6eTuC8iWjJhOLYJqpFjZNNOR\nYxDpBkHwR2IA+Add/QK2TU0uTz5psGuXOfqgb211WLIkxeuvh0gkDFw34wJySaUcbrwxPHr9vHkO\nxx7rTigUn11jIJNt1NtrTNquWslk0/jVtw2STTMdOQaRbhCEySmpAVBKPQnsTn/8m9b641nn3gdc\nAaSAm7TWN5RyLJPhF3R97DETMDjoIGf0WH+/weBg5sFhAC6GYbBkCbz4YoqXX7ZGs4CSSZtIJIRh\nGITDnlFIJk1eesnhTW9iQnZPNt5n/yygWiA7myae1rYPmk2THUC2zCaAvAHk6bQVhHqkZAZAKdUI\nGFrr433OhYFrgLcAMeBRpdRdWuvXSjWefPhJPGQCtuAFbE3Ty+3v7TXZd19YtMghkfDcMYYBzz5r\ncsYZNo6TYtcuaG6Gn/0shGka7LefO84ttGOHyYoVCQwjf42BoLUIqpXsbJq5rWEG+5OBAqrTKY4u\nhdQFYWpKuQLoApqVUvel+7lca/1f6XNLgBe01v0ASqlHgBXA7flu1traTChU2B9sW9vcCcdiMc9P\n39Q0dmx4eMzf39TkPYB37RrL/mlqGtvhm0h4LiTLgr339nT+X0ubsVBorH2GwUGABhYtKmgqvnOp\nVqJvCNYulojRtMNLHc0lnooztzVMNBKddttiUks/F5lL5VKs+ZTSAAwBVwM/AxYD9yqllNY6BcwD\nslVyBoFJRRP6+4cKGkxb21z6+gYnHLdtiMetcZk5jgMjI54LKB63GRnx2jlOiGQSEgl3NFMn4/O3\nbXtU98fz3YdIpUxSKXe0PgCAYThYVoK+vuLPpRqZzlxsxya+xyZhxn3PDfYnGTIHp922WNTrz6XS\nqaW5wPTnM5mxKGUW0HPAL7XWrtb6OWAnsCB9bjeQPaq5wK4SjiUvfgXgTRNaW11aW8cCtqEQdHQ4\nNDd7weFk0nv4O44nBWGa3mogk/Fz4IE2kYg7ri/b9to2TXwpFQKQLcfgODbDqWEcx/YNINesdEOt\n64MIs0opVwDnAMuAC5RS7Xhv/dvS554FFiul9gL24Ll/ri7hWCbFL+h69NHegyM7C+j0020eeMBk\n8+bxUhAXXZTiX/4lxKZNFomEQSTicuihNm9/e4pnnhnf9pJLpAB8ISzbp4vn+jXdr3czYg/TYDXS\nuVcnyxZ2TWhbU9INrou18Wmsnh5IJiAcwe7owF5+ODWhDyKUBcN13albzQClVAS4GTgQcIEvAIuA\nOVrr67OygEy8LKAfTna/vr7BggYaZNlk2xODrtnHNm/2soUcZ6z4u2nCAw8Y7NgRwrLG9gHYNixd\nmuKCC1Js3w777kvR3vxraUk73bls2P7UaCZPwk4Qsbw9Be3R9ryZPbZjz4p0Qyl/LtaGp7B6e8nN\nDbbb27G7ip/RVM+/Y5XODFxAed8QSrYC0FongA/nHH4s6/zdwN2l6n8m+BWAzxzLzhYyzfFB4E2b\nLPbff6x95v9Nm7w/1oULZ2kCNU5uZk9jaGzT2GSZPVVfSN22vTf/3FQw08Tq6cFeury20sSEWUN2\nAgckn0TDrl2QSPhr9IyMGKMxAaFw6lZGYnjYc/v4kUxUvz6IUDbEAAQkW6IhW/d//nyIRPx36DY0\nuMyfD319E/V/giDxvvHUbVGWxkYI56krGo5Uvz6IUDZECiIglgULFrisXWvS02OSShmEQi4dHQ6H\nHWbz+uuhcUbAtr2Uz+9/P8zIiLevoLPTZeXK1ASlz1xqvR7ATClURqJqsSzsjg7/GEBHh7h/hBkj\nK4Bp8MILJgMDFqZpEgoZmKb3+S1v8QK+qZRDLOZpAEGKRYu8dtGoQShkoLXJ2rVT29xsaYpMQLq3\n12DjRvlxLW87nPZouxfYTctItEfbqzOzZxrYyw/Hbm9PZyXEwba9APDy2p63UFpkBRCQTD2A9nYX\n1/U2d4VCXgbe888bfOELSWzb2wcwfz58//vhCXWCLcu7RyIxURE0g580BYzVA1i6tDTzqxbqtiiL\nYWB3HeEFfGtRH0QoC/JKGZDsegAZqeeMO2ZkxDvf1ORl/CQS/rUDstvmo9brARSLui3KkklLk4e/\nUATEAEyBXz2AXBoaxtJCwfs60zZTGCaz3SK3bS61Xg9AEITKQVxAefALxDY3uwwNGeNcO7btBXez\nXTqRiHfs4YcNhobGFEWbm11WrHDzun+g9usBCIJQOcgKIA9+gdjOTpfmZodUyk0He12Ucli5cqK8\nw8EHO7S0eBpDqZRnBFpaXA4+eGpN/+XLHdrb3dFdyLbtFaiplXoAgiBUBrIC8CFfIDYUgiVLXE44\nIcWePZ4rx+9t3rZh2zaDY4910plBnts2FPKOL59i42at1wMQBKEykBWAD1MFYh3H0/3P58rJvj4U\n8gxFxm00nUCuxPsEQSglYgB8KDQQK4FcQRCqgbowANOVVPCrEQBjgVjb9pd3yPQDk18vb/SCIFQC\nNR0DyM7kaWryKn8FlVTwqxGwYIHLc8+Z3HWXNU7e4fTTU2zePL5te7vLggXuuHoCtVTYXRCE6qem\nDUB2Jk9Tk/fG3ttrACZdXZM/iP0CsXfcEUJrk1BozKevtcG114Y49FDvzT7zdr9tm7dr+LTTbAnk\nCoJQkdSsCyiTyZMrvJaRVJiOOyhTD6C721+iYdMmc4K7J9MPSCBXEITKpGYNQLElFbKlILJJpTzd\n/4zvv9B+BEEQZouadQEVKxMnsxkrnxREKOTp/udWEptuP4IgCLNNzRqAQiUVgkpBOA4sW+ZMcDVJ\nxo8gCJVOSQ2AUmpf4AngFK11d9bxzwCfAPrShz6ptdbF7j87kyfuSagHzsTJDiBnHuKdnS7d3Q5D\nQ8aUWUCS8SMIQqVTMgOglAoDPwXiPqePBD6qtX6iVP3D+EyeuXNhcNAO9EY+EykIkW4QBKHaKOUK\n4GrgJ8CXfM4dCXxJKbUfcI/W+ptT3ay1tZlQqLCnajQ6N1C7WMxLG21qmnguHoe994YDDyxoKAXT\n1hZsLtWAzKUykblULsWaT0kMgFLqbKBPa71OKeVnAH4D/BDYDdyplHqv1vp3k92zv3+ooDG1tc2l\nr28wUFvb9jaN+RVyt21vJTFU2HAKYjpzqXRkLpWJzKVyme58JjMWpUoDPQc4RSn1IHA48Iv02z5K\nKQO4Vmu9Q2udAO4BjijROGZEthREKuWlgKZSY4FdmJ60hCAIQiVSkhWA1npF5uu0EfiU1vrV9KF5\nwGal1BIgBpwI3FSKcRTC0qUO998fYtMmk5ERg4YGl2XLHN7wBpd166wJwd6ppCUEQRAqjVlLA1VK\nfZn+8tsAAAdMSURBVBiYo7W+Xil1OfBHYARYr7X+/WyNIyh33BEiHjc55BBIpVxCIXjpJYs77oBj\njx1L7wwqLSEIglBplNwAaK2PT3/ZnXVsDbCm1H3PlETCk33I5PuHw96+gKEhgz17TFIpe/RcRvJh\n6VLJ/BEEobqoWSmIQvCTfcjEAFKpibIPIvkgCEI1UrcGYLIaAS0tE2UfQiHvbT8Umij7IJIPgiBU\nIzUrBZEPP4mH3EBuJOLt8NV6bDOYYXhSEC0tzgQpCJF8EAShGqm7FUC2xENmx25vr8HGjeO/FStX\nplDKIZVyicVcUimXFStsVq50RgXibNsr/CKSD4IgVCN1tQLIJ/HgF8g1TTjjjBSJhBcTyJZ9WL5c\nJB8EQah+6moFMJMaAZEItLWNPfxhrEiMPPwFQahm6soAFKtGgCAIQi1QVwYgW+IhGwnkCoJQj9RV\nDADG1wgQ7X5BEOqZujMA2TUCJJArCEI9U3cGIEMmkCsIglCv1FUMQBAEQRhDDIAgCEKdIgZAEASh\nThEDIAiCUKeIARAEQahTDNd1yz0GQRAEoQzICkAQBKFOEQMgCIJQp4gBEARBqFPEAAiCINQpYgAE\nQRDqFDEAgiAIdYoYAEEQhDqlLtRAlVJvBb6ltT6+3GMpBKVUGLgJWAQ0AF/XWt9V1kHNEKWUBdwA\nKMAFPqW13lzeURWGUmpf4AngFK11d7nHM1OUUk8Cu9Mf/6a1/ng5x1MISqkvAX8PRIAfaa1vLPOQ\nZoRS6mzg7PTHRuBwYD+t9a5C7lvzBkApdRlwFhAr91iKwD8BO7XWZyml9gKeBqrSAADvA9Bav0Mp\ndTzwDeD9ZR1RAaSN80+BeLnHUghKqUbAqPaXJYD079XRwDuAZuDSsg6oALTWNwM3AyilfgjcVOjD\nH+rDBfRX4PRyD6JI3A78a/prA0iVcSwFobX+LbAq/XEhUPAvc5m5GvgJ0FvugRRIF9CslLpPKfWA\nUupt5R5QAZwGbALuBO4Gflfe4RSOUuoo4DCt9fXFuF/NGwCt9VogWe5xFAOt9R6t9aBSai7wH8C/\nlHtMhaC1TimlbgF+ANxa7vHMlPTyvE9rva7cYykCQ3jG7DTgU8CtSqlq9RTsAxwFnMHYXIzyDqlg\nLgdWF+tmNW8Aag2l1AHAH4E1WutflXs8haK1/hhwCHCDUqpaa7SdA5yilHoQzzf7C6XUfuUd0ox5\nDvil1trVWj8H7AQWlHlMM2UnsE5rndBaa2AYaCvzmGaMUmo+oLTWfyzWPavVstclSqk3APcBF2mt\n15d7PIWglDoL2F9r/U28t04n/a/q0FqvyHydNgKf0lq/Wr4RFcQ5wDLgAqVUOzAP2FbeIc2YR4CL\nlVLfwzNiUTyjUK2sAIr6dy8GoLq4HGgF/lUplYkFvEtrXY2BxzuAnyulHgbCwCVVOo9a40bgZqXU\nI3jZWedorasy1qS1/p1SagXwP3jejgu11naZh1UICnixmDcUOWhBEIQ6RWIAgiAIdYoYAEEQhDpF\nDIAgCEKdIgZAEAShThEDIAiCUKeIARAEQCn1c6XUwinaPKiUOj7978ESj2e1UurY7H5L2Z9Qn4gB\nEASPE/D0lSqF4wCr3IMQahvZCCbUJOk35tV4OlAH4G0G+gTwj8AleC8/TwAXpj+3A79Pv3WfCHwO\naEr/+4TW+uGA/X4ROBPv4b0O+AKe2N2dwGbgCOA14Ayt9etKqTOBr+Hthn4S72/yATwNm58ppT6Q\nvvUnlFLfxdsIeLHW+u4ZfWMEIQtZAQi1zN/hPeA78TTULwXOA47WWh8ObAcu1Vpfhafi+W6gH084\n7L1a6y7gKuDzQTpTSr0TOBJ4C96DvgP4SPp0F/A9rfVSPOXTjyil2oBrgZPwHvh7AWitfwE8jmd4\nNqWv36W1PhL4NHDFjL4bgpCDrACEWubhtAgYSqk1ePITO4D/UkqBVyTkyewLtNZO+q37fcprdDwQ\nVD7gZOCteCsL8FYPL+Np0mzXWj+VPr4Z72F/LPBnrXVPeoy3AB/An9+m//8LnsqlIBSMGAChlsnW\nsDHx3DK3aa0/DaCUmkPO30D62P8Ca4CHgY3ARQH7s4BrtdbfS99rfnoM++ApUWZw8eINNsFX4Zm5\nZK4VhIIRF5BQyxyjlOpQSpnAR/F8/R9QSu2b1oX/cfoYeA/YEJ40tQNcieeLfxfBg7EPAGcppeak\nNfR/C3xwkvaPAW9RSi1Ij+dDeA/47PEIQsmQXzChlukFfoHni/8DcB1eadAH8F5+nsLz8YNXLer3\neA/8p4FuvMDsQ3hB3FyOVUrtyfr8S631p5RSXcB/4xmN/wfckud6tNZ9SqlPp8c2DLyEF4Mgfe1P\nlFIfnfasBSEgogYq1CTpLKCvVnJtW6XU3nhB3dXp2MP3gee11j8o89CEOkFWAIJQPl4H5gOblVIp\nvID0DeUdklBPyApAEAShTpEgsCAIQp0iBkAQBKFOEQMgCIJQp4gBEARBqFPEAAiCINQp/x/DkDqo\nrmfAvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110d9ae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for species, group in iris.groupby('Name'):\n",
    "    plt.scatter(group['PetalLength'], group['SepalLength'],\n",
    "                color=color_map[species],\n",
    "                alpha=0.3, edgecolor=None,\n",
    "                label=species)\n",
    "plt.legend(frameon=True, title='Name')\n",
    "plt.xlabel('petalLength')\n",
    "plt.ylabel('sepalLength')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1141570f0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u/qdm1gf83MyKlxV6H/C/3f1/mdkRwAYzOxboBf7R3X8R5Q8g6qzLFYWM7+75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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b623dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('PetalLength', 'SepalLength', iris, hue='Name', fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
